Different local approaches have been proposed in the literature, like Local classifier per Node (LCN) [1]}, Local classifier per parent node (LCPN), Local classifier per level (LCL) [2]}.
LCN strategy trains a local classifier for each node of a graph providing a local decision to make predictions.
LCPN uses a multi-class classifier for each internal class to recognize classes from its sub-classes and LCL methods train a multi-class classifier per hierarchical level.
In contrast with local (LCN, LCL, LCPN) and global approaches, we use a single trained model and a single back-propagation error with many different layers fully connected, responsible to synchronize with a concept linked to a given hierarchical structure.
